In recent years, indoor localization technology has been applied in the indoor navigation, social networks, advertisement pushing and other services. One of the necessary conditions for operation of an indoor location based service (LBS) system is the availability of an indoor map, so the automatic construction of the indoor map has become a main problem of the current LBS service. Many researchers use crowdsourced data (such as images, WiFi signal strength, motion trajectories of users and the like) to construct indoor two-dimensional plane maps. However, the two-dimensional plane maps constructed by these systems do not have semantic information of indoor spaces.
The emergence of an indoor semantic map can improve operation of the existing indoor LBS systems, and the indoor semantic map can also be used for designing new indoor LBS systems. The indoor semantic map records the spatial structure of an indoor object and its semantics (such as names, categories, functions and other non-spatial attributes), and each indoor space object have rich semantic information. Such indoor space objects refer to general entities at any locations and regions, including annotated entities and non-annotated entities. An annotated entity indicates that its semantic information has been labeled with text information, for example, the name and the functional attribute of the indoor object in a commercial place have been labeled with the text information. A non-annotated entity, for example, a fine-grained general entity, refers to an entity that lacks labeled text information.
Although the text information of each annotated entity can provide accurate semantic information for the automatic construction of the indoor semantic map, the method in the prior art mainly focuses on the recognition and classification of specific indoor entities, and such semantic text information cannot be accurately recognized. Furthermore, the semantic information of the indoor space is dynamically changing, for example, the change of stores in a mall, the update of the marketing information of the mall, and the change of exhibits. For a given indoor space, the semantic information already labeled on the map is obviously different from the current indoor semantic information. If the newly updated semantic information is not labeled in time or the out-of-date semantic information is not removed in time, the initial indoor semantic map can be gradually deteriorated or even break down the performance of the LBS system. In this case, the indoor semantic map not only can not improve the experience of the existing indoor LBS, but also cannot produce a new indoor LBS system. Therefore, the problem of adaptation and update of the indoor semantic map has not been solved. This open problem basically limits the application of the indoor semantic map, especially long-term deployment application.
A straightforward approach to solve this problem is to periodically regenerate the entire indoor semantic map. This method is time-consuming and laborious, and a lot of resources are wasted for updating the unchanged indoor environment. The method is designed to automatically construct the entire indoor plane map, is not suitable for real-time updating of clear semantic information of a complex indoor space. Moreover, it cannot update the text semantic information, so the update of the annotated entity will be invalid.
There is no effective solution to the problems that the cost of the method for updating the indoor semantic map in the prior art is too high, and that the text information cannot be updated.